棉纺织技术2025，Vol.53Issue(4)：59-66,8.
基于GSS-YOLOv8n的轻量化织物疵点检测算法
Lightweight fabric defect detection algorithm based on GSS-YOLOv8n
摘要
Abstract
Aiming at the problems of fabric defect detection,such as manual operation,time-consuming,complex background and many kinds of defects,a lightweight detection model GSS-YOLOv8n based on improved YOLOv8 algorithm was proposed.Firstly,GSConv was used to replace the original standard convolution core for speed and precision,and one-time aggregation method was used to design the cross-level partial network(GSCSP)module VoVGSCSP to replace the C2f module,the introduction of GSlim-Neck structure reduced the complexity of calculation and network structure,and maintained sufficient precision.Secondly,the YOLOv8 detector SCGD was redesigned to reduce the number of model parameters,while the missing rate of details was reduced.The performance of detection head localization and classification was improved,and the robustness of the model was improved.Finally,the loss function Shape-IoU was introduced,which considered the influence of the shape and size of the bounding box regression sample on the bounding box regression,making the IoU more accurate and robust.The results showed that mAP@0.5 and mAP@0.5∶0.95 of GSS-YOLOv8n model were 98.1%and 73.5%,which were 1.0 percentage points and 9.7 percentage points higher than the original model respectively,the parameters and computation were reduced by 34.7%and 35.4%respectively,and the detection speed reached 42.4 frames/s.GSS-YOLOv8n model could identify fabric defects accurately in real time based on the realization of lightweight.关键词
YOLOv8/织物疵点检测/Shape-IoU/轻量化/GSConvKey words
YOLOv8/fabric defect detection/Shape-IoU/lightweight/GSConv分类
轻工业引用本文复制引用
井振威,张团善..基于GSS-YOLOv8n的轻量化织物疵点检测算法[J].棉纺织技术,2025,53(4):59-66,8.基金项目
国家自然科学基金项目(51735010) （51735010）
